2020.2.27 Neural papers

07-21-2020	What is important about the No Free Lunch theorems? by David H. Wolpert
07-23-2020	Psychological, Computational and Robotic Models of Time Perception by Hamit Basgol et al
07-23-2020	HCMS at SemEval-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed Texts by Aditya Srivastava et al
07-24-2020	Deforming the Loss Surface by Liangming Chen et al
07-21-2020	Distributed Memory based Self-Supervised Differentiable Neural Computer by Taewon Park et al
07-22-2020	Adma: A Flexible Loss Function for Neural Networks by Aditya Shrivastava
07-24-2020	Dopant Network Processing Units: Towards Efficient Neural-network Emulators with High-capacity Nanoelectronic Nodes by Hans-Christian Ruiz Euler et al
07-23-2020	The Lottery Ticket Hypothesis for Pre-trained BERT Networks by Tianlong Chen et al
07-24-2020	Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains by Carola Figueroa-Flores et al
07-23-2020	Multi-Compartment Variational Online Learning for Spiking Neural Networks by Hyeryung Jang et al
07-23-2020	Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems by Maurizio Ferrari Dacrema et al
07-23-2020	Dimension reduction in recurrent networks by canonicalization by Lyudmila Grigoryeva et al
07-23-2020	Image-Based Benchmarking and Visualization for Large-Scale Global Optimization by Kyle Robert Harrison et al
07-23-2020	The Representation Theory of Neural Networks by Marco Antonio Armenta et al
07-23-2020	Revisiting Locality in Binary-Integer Representations by Hrishee Shastri et al